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import gradio as gr
import torch
import cv2
import numpy as np
from torchvision import transforms
from PIL import Image
from transformers import DPTForDepthEstimation, DPTFeatureExtractor
# Load depth estimation model
model_name = "Intel/dpt-large"
feature_extractor = DPTFeatureExtractor.from_pretrained(model_name)
depth_model = DPTForDepthEstimation.from_pretrained(model_name)
depth_model.eval()
def estimate_depth(image):
"""Estimate depth map from image."""
image = image.convert("RGB")
image = image.resize((384, 384)) # Resize for model input
inputs = feature_extractor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = depth_model(**inputs)
depth = outputs.predicted_depth.squeeze().cpu().numpy()
depth = cv2.resize(depth, (image.width, image.height)) # Resize back to original
depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255
return depth.astype(np.uint8)
def warp_design(cloth_img, design_img):
"""Warp the design onto the clothing while preserving folds."""
cloth_img = cloth_img.convert("RGB")
design_img = design_img.convert("RGB")
cloth_np = np.array(cloth_img)
design_np = np.array(design_img)
# Estimate depth for fold detection
depth_map = estimate_depth(cloth_img)
depth_map = cv2.resize(depth_map, (cloth_np.shape[1], cloth_np.shape[0])) # Ensure matching dimensions
# Resize design to fit centrally on clothing
design_h, design_w = cloth_np.shape[:2]
center_x, center_y = design_w // 2, design_h // 2
resized_design = cv2.resize(design_np, (design_w // 2, design_h // 2))
# Place design at the center of the clothing image
y_offset = center_y - resized_design.shape[0] // 2
x_offset = center_x - resized_design.shape[1] // 2
blended_design = cloth_np.copy()
blended_design[y_offset:y_offset + resized_design.shape[0], x_offset:x_offset + resized_design.shape[1]] = resized_design
# Generate displacement map based on depth
displacement_x = cv2.Sobel(depth_map, cv2.CV_32F, 1, 0, ksize=5)
displacement_y = cv2.Sobel(depth_map, cv2.CV_32F, 0, 1, ksize=5)
# Normalize displacement values
displacement_x = cv2.normalize(displacement_x, None, -5, 5, cv2.NORM_MINMAX)
displacement_y = cv2.normalize(displacement_y, None, -5, 5, cv2.NORM_MINMAX)
# Warp design using displacement map
h, w, _ = cloth_np.shape
map_x, map_y = np.meshgrid(np.arange(w), np.arange(h))
map_x = np.clip(np.float32(map_x + displacement_x), 0, w - 1)
map_y = np.clip(np.float32(map_y + displacement_y), 0, h - 1)
warped_design = cv2.remap(blended_design, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT)
# Blend images
blended = cv2.addWeighted(cloth_np, 0.6, warped_design, 0.4, 0)
return Image.fromarray(blended)
def main(cloth, design):
return warp_design(cloth, design)
iface = gr.Interface(
fn=main,
inputs=[gr.Image(type="pil"), gr.Image(type="pil")],
outputs=gr.Image(type="pil"),
title="AI Cloth Design Warping",
description="Upload a clothing image and a design to blend it naturally at the center, considering fabric folds."
)
if __name__ == "__main__":
iface.launch(share=True) |